summary.glm {base} | R Documentation

## Summarizing Generalized Linear Model Fits

### Description

These functions are all `methods` for class `glm` or `summary.glm` objects.

### Usage

    ## S3 method for class 'glm':
    summary(object, dispersion = NULL, correlation = FALSE,
           symbolic.cor = FALSE, ...)

    ## S3 method for class 'summary.glm':
    print(x, digits = max(3, getOption("digits") - 3),
          symbolic.cor = x$symbolic.cor,
          signif.stars = getOption("show.signif.stars"), ...)

### Arguments

`object` | an object of class `"glm"`, usually, a result of a call to `glm`.
`x` | an object of class `"summary.glm"`, usually, a result of a call to `summary.glm`.
`dispersion` | the dispersion parameter for the fitting family. By default it is obtained from `object`.
`correlation` | logical; if `TRUE`, the correlation matrix of the estimated parameters is returned and printed.
`digits` | the number of significant digits to use when printing.
`symbolic.cor` | logical. If `TRUE`, print the correlations in a symbolic form (see `symnum`) rather than as numbers.
`signif.stars` | logical. If `TRUE`, “significance stars” are printed for each coefficient.
`...` | further arguments passed to or from other methods.

### Details

`print.summary.glm` tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives “significance stars” if `signif.stars` is `TRUE`.

Aliased coefficients are omitted in the returned object but (as from R 1.8.0) restored by the `print` method.

Correlations are printed to two decimal places (or symbolically): to see the actual correlations print `summary(object)$correlation` directly.

### Value

`summary.glm` returns an object of class `"summary.glm"`, a list with components

`call` | the component from `object`.
`family` | the component from `object`.
`deviance` | the component from `object`.
`contrasts` | the component from `object`.
`df.residual` | the component from `object`.
`null.deviance` | the component from `object`.
`df.null` | the component from `object`.
`deviance.resid` | the deviance residuals: see `residuals.glm`.
`coefficients` | the matrix of coefficients, standard errors, z-values and p-values. Aliased coefficients are omitted.
`aliased` | named logical vector showing if the original coefficients are aliased.
`dispersion` | eother the supplied argument or the estimated dispersion if the latter in `NULL`
`df` | a 3-vector of the rank of the model and the number of residual degrees of freedom, plus number of non-aliased coefficients.
`cov.unscaled` | the unscaled (`dispersion = 1`) estimated covariance matrix of the estimated coefficients.
`cov.scaled` | ditto, scaled by `dispersion`.
`correlation` | (only if `correlation` is true.) The estimated correlations of the estimated coefficients.
`symbolic.cor` | (only if `correlation` is true.) The value of the argument `symbolic.cor`.

### See Also

`glm`, `summary`.

### Examples

    ## --- Continuing the Example from  '?glm':

    summary(glm.D93)

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